In cellular networks, e.g., as specified by 3GPP (3rd Generation Partnership Project), increasing traffic demand results in a need for more radio spectrum bandwidth. One way to provide more radio spectrum bandwidth is expansion into unlicensed frequency spectra, e.g., as typically used by WLAN (Wireless Local Area Network) technologies. For example, in 3GPP meeting contribution RP-140240, 3GPP TSG RAN Meeting #63, Fukuoka, Japan, 3-6 Mar. 2014, it is proposed to study extension of the LTE (Long Term Evolution) radio technology for operation in unlicensed frequency bands.
In an unlicensed frequency band, typically more bandwidth than the maximum standardized LTE carrier bandwidth of 20 MHz is available. Accordingly, the conventional practice of running all LTE base stations of a network on the same frequency may be suboptimal because the larger available bandwidth allows for reducing intra-cell interference by distributing base stations over multiple different frequency channels. Further, channel quality in unlicensed frequency bands may vary depending on time, location and/or frequency, which means that also the optimum selection of the frequency channel may vary for each base station. Therefore, it is generally desirable to perform selection of the frequency channels for the base stations on the basis of a constantly running automated algorithm.
In a general context, the problem of frequency channel selection has been studied intensively for a long time and many different algorithms have been proposed. On a high level, one may distinguish algorithms that are intended for offline frequency planning of cellular networks like in the case of GSM (Global System for Mobile Communications), and real-time algorithms that are intended to be implemented as a Self-optimizing Network (SON) feature in base stations. The main difference between these two categories is the amount of input information they require and the computation time until they deliver results. Offline algorithms are typically allowed to run for a very long time, e.g., hours or days, and can afford a significantly higher computational complexity, while real-time algorithms should deliver results in seconds or faster, and may need to cope with limited input information.
Frequency selection algorithms can be implemented in a distributed or a centralized way. Here, distributed means that independent algorithm instances run, e.g., in each base station. The different algorithm instances influence each other for example in terms of how much interference another instance sees on a given channel. In a centralized approach, all information is gathered in a central location, which allows for a more complete assessment of the overall situation and facilitates finding an optimal solution. Distributed algorithms have a higher risk of being trapped in local minima and are typically iterative, i.e., which means that the system typically runs through a number of suboptimal stages before it may reach a steady state. In a centralized algorithm, even if it is based on iterations, system operation can converge in one step. Further, a centralized algorithm is more likely to find a global optimum because information from various parts of the network can be considered. In each case, finding an algorithm which offers a suitable tradeoff between system performance gain, computational complexity, execution time, signaling overhead, and other aspects is a complex task.
Accordingly, there is a need for techniques which allow for efficiently controlling frequency channel utilization in a cellular network.